import argparse
import os
import numpy as np
import math

import torchvision.transforms as transforms
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
import torch
import logging

os.makedirs("/workspace/GAN-Learning/HW3-1/gan/images", exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

logging.basicConfig(level=logging.INFO,
                    filename='/workspace/GAN-Learning/HW3-1/train_state.log',
                    filemode='a',
                    format='%(message)s'
                    )

parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=96, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=200, help="interval betwen image samples")
parser.add_argument("--d_times", type=int, default=1, help="Learning D: Repeat d_times")
opt = parser.parse_args()
# print(opt)

img_shape = (opt.channels, opt.img_size, opt.img_size)


class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)   # torch.Size([64, 3*96*96])
        img = img.view(img.size(0), *img_shape)  # torch.Size([64, 3, 96, 96])
        return img


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 1024),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(1024, 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)

        return validity


# Loss function
adversarial_loss = torch.nn.BCELoss()

# Initialize generator and discriminator
generator = Generator().to(device)
discriminator = Discriminator().to(device)

# Configure data loader
train_data_path = '/workspace/GAN-Learning/Anime_data/'
train_set = datasets.ImageFolder(root=train_data_path, transform=transforms.Compose(
            [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]))
dataloader = torch.utils.data.DataLoader(train_set, batch_size=opt.batch_size, shuffle=True)

# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

Tensor = torch.FloatTensor

# ----------
#  Training
# ----------

for epoch in range(opt.n_epochs):
    for i, (imgs, _) in enumerate(dataloader):
        imgs = imgs.to(device)
        # Adversarial ground truths
        valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False).to(device)
        fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False).to(device)

        # Configure input
        real_imgs = Variable(imgs.type(Tensor)).to(device)
        # Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim)))).to(device)
        # Generate a batch of images
        gen_imgs = generator(z)

        # ---------------------
        #  Train Discriminator
        # ---------------------
        for k in range(opt.d_times):
            optimizer_D.zero_grad()

            # Measure discriminator's ability to classify real from generated samples
            real_loss = adversarial_loss(discriminator(real_imgs), valid)
            fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
            d_loss = (real_loss + fake_loss) / 2

            d_loss.backward()
            optimizer_D.step()

        # -----------------
        #  Train Generator
        # -----------------
        optimizer_G.zero_grad()
        # Sample noise as generator input
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim)))).to(device)
        # Generate a batch of images
        gen_imgs = generator(z)
        # Loss measures generator's ability to fool the discriminator
        g_loss = adversarial_loss(discriminator(gen_imgs), valid)

        g_loss.backward()
        optimizer_G.step()

        print("[Epoch {}/{}] [Batch {}/{}] [D loss: {}] [G loss: {}]".format(epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()))
        logging.info("[Epoch {}/{}] [Batch {}/{}] [D loss: {}] [G loss: {}]".format(epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item()))

        batches_done = epoch * len(dataloader) + i
        if batches_done % opt.sample_interval == 0:
            save_image(gen_imgs.data[:25], "/workspace/GAN-Learning/HW3-1/gan/images/%d.png" % batches_done, nrow=5, normalize=True)
